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1.
新型建筑工业化具有高质量、低消耗、可循环发展等特征,其推广已上升到国家战略层面。利用演化博弈方法,建立“政府-开发商-银行” 的三方动态演化博弈模型,进行各博弈主体策略的演化稳定性分析,并针对初始状态、奖惩力度、借贷风险和开发成本等对演化结果的影响进行动态仿真。在此基础上, 考虑开发商群体的网络拓扑特征对演化真实性的影响,引入复杂网络理论, 以无标度网络为载体描述开发商个体的连接偏好和决策机制,构建政府监管下的建筑工业化扩散模型,并通过仿真深入研究相关因素对扩散深度的影响作用,最后结合仿真结果给出相应对策建议。 相似文献
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This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California. 相似文献
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Wesley W. Chu Ph.D. Ion T. leong Ph.D. Ricky K. Taira Ph.D. 《The VLDB Journal The International Journal on Very Large Data Bases》1994,3(4):445-477
We introduce a semantic data model to capture the hierarchical, spatial, temporal, and evolutionary semantics of images in pictorial databases. This model mimics the user's conceptual view of the image content, providing the framework and guidelines for preprocessing to extract image features. Based on the model constructs, a spatial evolutionary query language (SEQL), which provides direct image object manipulation capabilities, is presented. With semantic information captured in the model, spatial evolutionary queries are answered efficiently. Using an object-oriented platform, a prototype medical-image management system was implemented at UCLA to demonstrate the feasibility of the proposed approach. 相似文献
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Many real-world problems are multi-objective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multi-objective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a single-objective problem can be solved more efficiently via a generalized multi-objective model of the problem. That this is indeed the case is proved by investigating the computation of minimum spanning trees.This work was supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Center Computational Intelligence (SFB 531) and by the German-Israeli Foundation (GIF) in the project Robustness Aspects of Algorithms. 相似文献
6.
一种新的基于极大似然估计的系统辨识方法 总被引:1,自引:0,他引:1
进化计算是一种基于自然选择和自然变异机制的并行随机优化算法。由于递推极大似然估计法难以实现性能指标函数的全局寻优,所以本文提出了将改进的进化计算用于系统参数辨识的方法。计算机仿真结果表明,该算法能够实现似然函数的全局寻优。 相似文献
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Six misconceptions about evolutionary approaches to human behavior are exposed. Evidence is adduced to support the assertions that evolutionary approaches do not (a) adopt a reductionistic "gene-centered" level of analysis, (b) assume that natural selection is the only process that creates and designs ontogenetic processes and phenotypic outcomes, (c) assume that genes are the only agents responsible for the transgenerational inheritance of phenotypic traits and characteristics, (d) assume that genes are self-contained and impervious to extragenetic influences, (e) posit a strong form of genetic determinism, or (f) pay lip service to the role of the environment. Building straw men and knocking them down is an inherently destructive enterprise; integrating different approaches is a more constructive way of contributing to the growth of knowledge. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
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为实现对个体的兴趣建模,提出基于进化计算的建模(Evolutionary Computation Based Modeling,ECBM)方法。该方法采用定性和定量相结合的方式对个体的兴趣系统进行建模,首先通过定性分析构建个体的兴趣模型框架,然后通过进化计算(Evolutionary Computation)方法对模型参数自动进化。实验结果表明,ECBM能够通过数据驱动模式优化模型参数,并利用更新信息进化模型,使其不断逼近真实系统。 相似文献
9.
ZHAO Jing HAN ChongZhao & WEI Bin Ministry of Education Key Lab For Intelligent Networks Network Security State 《中国科学:信息科学(英文版)》2012,(11):2485-2494
This paper introduces a novel variation of binary particle swarm optimization(BPSO) algorithm and a further extension to improve its performance.Firstly,mimicking the behaviors of some creatures group,multiple evolutionary strategies BPSO(MBPSO) is introduced which takes different evolutionary strategies for various particles according to their performances.Then,on the basis of MBPSO,a new strategy is discussed to improve the performance of the MBPSO(M2BPSO) which adopts the concept of the mutation operator aiming to overcome the premature convergence and slow convergent speed during the later stages of the optimization.The proposed two algorithms are tested on seven benchmark functions and their results are compared with those obtained by other methods.Experimental results show that our methods outperform the other algorithms. 相似文献
10.
Biogeography-based optimization (BBO) is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization (BBMO) is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability effciently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II (NSGA-II) and archive-based micro genetic algorithm (AMGA) in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do. 相似文献